Triple

T25814866
Position Surface form Disambiguated ID Type / Status
Subject judiciary of Egypt E650220 entity
Predicate hasComponent P35 FINISHED
Object primary courts of Egypt
The primary courts of Egypt are the first-instance trial courts that handle most civil and criminal cases within the Egyptian judicial system.
 E1492197 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: primary courts of Egypt | Statement: [judiciary of Egypt, hasComponent, primary courts of Egypt]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: primary courts of Egypt
Triple: [judiciary of Egypt, hasComponent, primary courts of Egypt]
Generated description
The primary courts of Egypt are the first-instance trial courts that handle most civil and criminal cases within the Egyptian judicial system.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69e7ab367fcc8190a5ff1e7f3da046a4 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f600c84ac4819091492e52a5a8b873 completed May 2, 2026, 1:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10da1fb11c8190b04a22f08ae54848 completed May 22, 2026, 10:35 p.m.
NEDg Description generation batch_6a10dd1a43e48190992000544b46724b completed May 22, 2026, 10:47 p.m.
NED2 Entity disambiguation (via description) batch_6a10df81838881908011d553f260e7a0 completed May 22, 2026, 10:58 p.m.
Created at: April 22, 2026, 7:25 a.m.